An application of multiple‐point statistics downscaling approach over North‐West Himalayas in avalanche‐prone areas

Abstract

Weather data at fine spatial resolution in the Himalayan region are important for assessing various hydrological events such as avalanches. In recent years, national agencies have been exploring the use of Weather Research and Forecasting (WRF) model in this region. Setting up a WRF model with fine spatial grids in the Himalayan region is challenging due to large topographical variations and associated computational cost. In this study, we use High Asia Refined analysis (HAR) dataset to set up a Multiple-Point Statistics (MPS) based downscaling approach using the hindcast data of years 2001-2012 at both coarse and fine spatial grids. A total of 11 covariates are used in the study belonging to various environmental factors such as weather, location, topography and vegetation. We generate multiple realizations of precipitation and

Editorial: Extreme precipitation events: Spatio-temporal connections, forecasting, generation, impact analysis, vulnerability and risk assessment

“…Unfortunately, the frequency of EPEs is increasing and is estimated to even intensify in the future (Ali and Mishra, 2018). During the past decades, studies have used various approaches to understand the mechanism behind the formation of EPEs, their spatio-temporal characteristics, their impacts on various sectors of society, and ways to prevent adverse impacts (Singhal and Jha, 2021). On this vital research topic of Extreme Precipitation Events, 12 articles were accepted and published.…”